Genetics for Epidemiologists Lecture 5: Analysis of Genetic Association Studies

Genetics for Epidemiologists

Lecture 5: Analysis of Genetic Association Studies

National Human Genome Research

Institute

National Institutes of

Health

U.S. Department of Health and

Human Services

U.S. Department of Health and Human Services

National Institutes of HealthNational Human Genome Research

InstituteTeri A. Manolio, M.D., Ph.D.

Director, Office of Population Genomics andSenior Advisor to the Director, NHGRI,

for Population Genomics

Topics to be Covered

• Discrete traits and quantitative traits

• Measures of association

• Detecting/correcting for false positives

• Genotyping quality control

• Quantile-quantile (Q-Q) plots

• Odds ratios: allelic and genotypic

• Models of genetic transmission

• Interactions: gene-gene, gene-environment

Larson, G. The Complete Far Side. 2003.

Quantitative Genetics

“…concerned with the inheritance of those differences between individuals that are of degree rather than of kind…”

Quantitative Qualitative

Falconer and Mackay, Quantitative Genetics 1996.




Continuous gradation among individuals from one extreme to other

Sharply demarcated types with little connection by intermediates







Effects of genes are small

Effects of genes are large







Effects of genes are small

Effects of genes are large

Usually many genes Single genes inherited in Mendelian ratios?


Inheritance Models in Single Gene Trait

A

a

Genotype Group

Model AA Aa aa


Genotype Group

Model AA Aa aa

A is Dominant


Genotype Group

Model AA Aa aa

A is Dominant


Genotype Group

Model AA Aa aa

A is Dominant

A is Recessive


Genotype Group

Model AA Aa aa

A is Dominant

A is Recessive

A is Co-Dominant


Inheritance Models in Quantitative Trait

A x increase in height

a x decrease in height

Population Mean

Model -x 0 +x


Population Mean

Model -x 0 +x

A is Completely Dominant

aa

AAAa


Population Mean

Model -x 0 +x


aa

AAAa

A is Partially Dominant

aa Aa AA


Population Mean

Model -x 0 +x


aa

AAAa


aa Aa AA

A is Not (Co-) Dominant

aa Aa AA


Population Mean

Model -x 0 +x


aa

AAAa


aa Aa AA

A is Not (Co-) Dominant

aa Aa AA

A is Over-Dominant

aa AA Aa


Quantitative Traits with Published GWA Studies (16 - 34)

•QT interval •Lipids and lipoproteins

•Memory•Nicotine dependence

•ORMDL3 expression•YKL-40 levels •Obesity, BMI, waist•Insulin resistance•Height

•Bone mineral density•F-cell distribution•Fetal hemoglobin levels

•C-Reactive protein•18 groups of Framingham traits

•Pigmentation•Uric Acid Levels•Recombination Rate

Association of Alleles and Genotypes of rs1333049 (‘3049) with Myocardial

Infarction C

N (%)G

N (%)2

(1df)P-value

Cases2,132 (55.4)

1,716 (44.6)

55.11.2 x 10-

13Controls

2,783 (47.4)

3,089 (52.6)

Allelic Odds Ratio = 1.38

Samani N et al, N Engl J Med 2007; 357:443-453.

Association of Alleles and Genotypes of rs1333049 (‘3049) with Myocardial

Infarction C

N (%)G

N (%)2

(1df)P-value

Cases2,132 (55.4)

1,716 (44.6)

55.11.2 x 10-

13Controls

2,783 (47.4)

3,089 (52.6)

Allelic Odds Ratio = 1.38CC

N (%)CG

N (%)GG

N (%)2

(2df) P-value

Cases586

(30.5) 960 (49.9)

378 (19.6)59.7

1.1 x 10-

14Controls

676 (23.0)

1,431 (48.7)

829 (28.2)

Heterozygote Odds Ratio = 1.47

Homozygote Odds Ratio = 1.90


-Log10 P Values for SNP Associations with Myocardial

Infarction


http://www.broad.mit.edu/diabetes/scandinavs/type2.html

Genome-Wide Scan for Type 2 Diabetes in a Scandinavian Cohort

• Linear regression of inverse normalized levels against number of alleles

• Additive model• Sex, age, age2 as covariates

GWA Study of Serum Uric Acid Levels

Li S et al, PLoS Genet 2007; 3:e194.

Association of rs6855911 and Uric Acid Levels

Li S et al, PLoS Genet 2007; 3:e194.

Genotype Means (mg/dl)

Cohort Additive Effect

AA AG GG

SardiNIA -0.317 4.66 (1.51)

4.48 (1.59)

4.02 (1.63)

InCHIANTI

-0.397 5.27 (1.44)

4.94 (1.31)

4.33 (1.37)

Association Methods for Quantitative Traits

• Linear regression of multivariable adjusted residual against number of alleles (Kathiresan,Nat Genet 2008; 40:189-97)

• Linear regression of log transformed or centralized BMI against genotype (Frayling, Science 2007; 316:889-94)

• Variance components based Z-score analysis of quantile normalized height (Sanna, Nat Genet 2008; 40:198-203)

Ways of Dealing with Multiple Testing

• Control family wise error rate (FWER): Bonferroni (α’ = α/n) or Sĭdák (α’ = 1- [1- α]1/n)

• False discovery rate: proportion of significant associations that are actually false positives

• False positive report probability: probability that the null hypothesis is true, given a statistically significant finding

• Bayes factors analysis: avoids need for assessing genome-wide error rates but must identify reasonable alternative model

Hogart CJ et al, Genet Epidemiol 2008; 32:179-85.


Quality Control of SNP Genotyping: Samples

• Identity with forensic markers (Identifiler)

• Blind duplicates

• Gender checks

• Cryptic relatedness or unsuspected twinning

• Degradation/fragmentation

• Call rate (> 80-90%)

• Heterozygosity: outliers

• Plate/batch calling effects

Chanock et al, Nature 2007; Manolio et al Nat Genet 2007

Quality Control of SNP Genotyping: SNPs

• Duplicate concordance (CEPH samples)

• Mendelian errors (typically < 1)

• Hardy-Weinberg errors (often > 10-5)

• Heterozygosity (outliers)

• Call rate (typically > 98%)

• Minor allele frequency (often > 1%)

• Validation of most critical results on independent genotyping platform

Chanock et al, Nature 2007; Manolio et al Nat Genet 2007

Hardy-Weinberg Equilibrium

• Occurrence of two alleles of a SNP in the same individual are two independent events

• Ideal conditions:– random mating - no selection (equal

survival)– no migration - no mutation– no inbreeding - large population sizes– gene frequencies equal in males and females)…

• If alleles A and a of SNP rs1234 have frequencies p and 1-p, expected frequencies of the three genotypes are:

After G. Thomas, NCI

Freq AA = p2 Freq Aa = 2p(1-p) Freq aa = (1-p)2

Metric Perlegen Affymetrix/Broad

Number of SNPs 480,744 439,249

Coverage Single Marker

Multi-Marker

Single Marker

Multi-Marker

CEU 0.90 0.96 0.78 0.87 CHB + JPT 0.87 0.93 0.78 0.86 YRI 0.64 0.78 0.63 0.75Average call rate 98.9% 99.3%

Concordance

Homozygous genotypes 99.8% 99.9%

Heterozygous genotypes 99.8% 99.8%

Coverage, Call Rates, and Concordance of Perlegen and Affymetrix Platforms on

HapMap Phase II

GAIN Collaborative Group, Nat Genet 2007; 39:1045-51.

Metric 5.0 % fail 6.0 % failTotal Samples 1,829 -- 2,289 --Passing QC 1,817 0.44 2,192 4.24> 98% call rate 1,815 0.55 2,257 1.40

Sample and SNP QC Metrics for Affymetrix 5.0 and 6.0 Platforms in GAIN

Courtesy, J Paschall, NCBI

Metric 5.0 % fail 6.0 % failTotal Samples 1,829 -- 2,289 --Passing QC 1,817 0.44 2,192 4.24> 98% call rate 1,815 0.55 2,257 1.40

Total SNPs 457,645 -- 906,660 --Passing QC 429,309 6.19 845,814 6.70MAF > 1% 457,466 0.04 888,234 2.03> 98% call rate 419,810 8.27 821,942 9.34> 95% call rate 439,272 4.01 873,856 3.61HWE < 10 -6 455,899 0.38 904,275 0.26< 1 Mendel error 417,722 8.72 899,721 0.01

< 1 Duplicate error 454,820 0.01 892,103 0.02

Sample and SNP QC Metrics for Affymetrix 5.0 and 6.0 Platforms in GAIN


Sample Heterozygosity in GAIN

0

500

1,000

1,500

2,000

2,500

0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40

Fre

quency


Sample Heterozygosity in GAIN

0

10

20

30

40

50

60

70

80

90

100

0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40

Fre

quency


Signal Intensity Plots for rs10801532 in AREDS

http://www.ncbi.nlm.nih.gov/sites/entrez







Signal Intensity Plots for CD44 SNP rs9666607

Clayton DG et al, Nat Genet 2005; 37:1243-1246.

Courtesy, G. Thomas, NCI

Principal Component Analysis of Structured Population: First to Third

Components


Principal Component Analysis of Structured Population: Fourth and

Fifth Components


Influence of Relatedness on Principal Component Analysis



Fifth Components



Fifth Components

Summary Points: Genotyping Quality Control

• Sample checks for identity, gender error, cryptic relatedness

• Sample handling differences can introduce artifacts but probably can be adjusted for

• Association analysis is often quickest way to find genotyping errors

• Low MAF SNPs are most difficult to call

• Inspection of genotyping cluster plots is crucial!

Easton D et al, Nature 2007; 447:1087-1093.

Quantile-Quantile Plot for Test Statistics,

390 Breast Cancer Cases, 364 Controls

205,586 SNPsλ = 1.03

Easton D et al, Nature 2007; 447:1087-93.

Observed and Expected Associations after Stage 2 of Breast

Cancer GWASignificance

Observed

Observed

Adjusted

Expected

Ratio

0.01 - 0.05 1,239 1,162 934 1.24

10-3 – 10-2 574 517 348 1.49

10-4 – 10-3 112 88 53 1.65

10-5 – 10-4 16 12 7 1.71

< 10-5 15 13 1 13.5

All p < 0.05 1,956 1,792 1,343 1.33

Q-Q Plot for Multiple Sclerosis; Effect of MHC

Hafler D et al, N Engl J Med 2007; 357:851-862.

Q-Q Plot for Prostate Cancer, all SNPs

Gudmundsson J et al, Nat Genet 2007; 39:977-983.

Q-Q Plot for Prostate Cancer, excluding Chromosome 8

Gudmundsson J et al, Nat Genet 2007; 39:977-983.

Q-Q Plot for Myocardial Infarction


Expected chi-squared statistic0 5 10 15 20 25

Obs

erve

d ch

i-squ

ared

sta

tistic

0

2

0

40

60


Infarction



Infarction


SNP Associations with 1,928 MI Cases and 2,938 Controls from UK


Association Signal for Coronary Artery Disease on Chromosome 9

’3049


Winner’s Curse: Odds Ratios for CHD Associated with LTA Genotypes in

Multiple Studies

Clarke et al, PLoS Genet 2006; 2:e107.

Genome-Wide Scan for Alzheimer’s Disease in 861 Cases and 550

Controls

Reiman E et al, Neuron 2007; 54:713-20.

Genome-Wide Scan for Alzheimer’s Disease in ApoE*e4Carriers

Reiman E et al, Neuron 2007; 54:713-20.

LOAD Odds Ratios Associated with rs2373115 GG by APOE*e4 Status

APOE*e4 Group

APOE*e4 OR [95% CI]

rs2373115OR [95%CI]

APOE*e4 - 1.12 [0.82,1.53]

APOE*e4 + 2.88 [1.90,4.36]

All6.07 [4.63-

7.95]1.34 [1.06,1.70]

Reiman et al, Neuron 2007; 54:713-720.

Klein et al, Science 2005; 308:385-389.

P Values of GWA Scan for Age-Related Macular Degeneration

Klein et al, Science 2005; 308:385-389.

Odds Ratios and Population Attributable Risks for AMD

Attribute (SNP) rs380390

(C/G) rs1329428

(C/T)

Risk allele C C

Allelic association χ2 P value 4.1 x 10–8 1.4 x 10–6

Odds ratio (dominant) 4.6 [2.0-11] 4.7 [1.0-22]

Frequency in HapMap CEU 0.70 0.82

Population Attributable Risk

70% [42-84%] 80% [0-96%]

Odds ratio (recessive) 7.4 [2.9-19] 6.2 [2.9-13]

Frequency in HapMap CEU 0.23 0.41

Population Attributable Risk

46% [31-57%]

61% [43-73%]

Risk of Developing AMD by CFH Y402H and Modifiable Risk Factors

Schaumberg DA et al, Arch Ophthalmol 2007; 125:55-62.

Risk Factor

CFH Y402H Genotype

YY YH HH

BMI < 30 kg/m2 1.00 1.95

[1.42-2.67]3.96

[2.69-5.82]

BMI > 30 kg/m2

1.98 [0.91-4.31]

2.19 [1.11-4.30]

12.28 [4.88-30.90]

Non-smoker 1.00 1.95 [1.41-2.71]

4.23 [2.86-6.27]

Current smoker

2.34 [1.20-4.55]

3.20 [1.85-5.55]

8.69 [3.86-19.57]

TT

CC

CT

Ordovas et al, Circulation 2002; 106:2315-2321.

Interaction: Is LIPC Genotype Related to HDL-C?

TT CC

CT

Inverse Relation between Endotoxin Exposure and Allergic Sensitization

by CD14 Genotype

Simpson A et al, Am J Respir Crit Care Med 2006;174:386-392.

Challenges in Studying Gene-Environment Interactions

Challenge GenesEnvironme

nt

Ease of measure Pretty easy Often hard

Variability over time

Low/none High

Recall bias None Possible

Temporal relation to disease

Easy Hard


Documents

Genetics for Epidemiologists Lecture 5: Analysis of Genetic Association Studies